Hello, I am using WGCNA to construct co-expression networks.

Scripts:

#!/usr/bin/Rscript
getwd()
workingDir = "."
setwd(workingDir)
library(WGCNA)
options(stringsAsFactors = FALSE)
enableWGCNAThreads()
lnames = load(file ="dataInput.RData")
lnames = load(file ="networkConstruction.RData")
nGenes = ncol(datExpr)
nSamples = nrow(datExpr)
softpower = 6
dissTOM = 1 - TOMsimilarityFromExpr(datExpr, power = softpower)
nSelect = 1500
set.seed(10)
select = sample(nGenes, size = nSelect)
selectTOM = dissTOM[select, select]
selectTree = hclust(as.dist(selectTOM), method = "average")
selectColors = moduleColors[select]
plotDiss = selectTOM^7
diag(plotDiss) = NA
pdf(file = "Plots/networkHeatmap.pdf", width = 15, height = 15)
TOMplot(plotDiss, selectTree, selectColors, main = "Network heatmap plot, selected genes")
dev.off()
save(dissTOM, file = "dissTOM.RData")
MEs = moduleEigengenes(datExpr, moduleColors)$eigengenes
MET = orderMEs(MEs)
pdf(file = "Plots/hubGeneHeatmap.pdf", width = 6, height = 6)
plotEigengeneNetworks(MET, "Eigengene adjacency heatmap", marHeatmap = c(3,4,2,2), plotDendrograms = FALSE, xLabelsAngle = 90)
dev.off()

And I got a error:

Error in hclust(as.dist(selectTOM), method = "average") : 
  NaN dissimilarity value.
Calls: hclust -> .Call
Execution halte

I double checked and I am sure there is non-numeric fpkm data in my input file. Does anyone meet this problem and solved it?

Thank you!

My guess is that your data contain some non-varying (constant) genes. Try running goodSamplesGenes on datExpr, then check the component allOK of the result. If that is FALSE, you will need to remove genes with zero variance as illustrated in section 1b of Tutorial I of the WGCNA tutorials.

Well, I would run

adj = adjacency(datExpr)

and check whether there are any missing or NaN values. If there are any, get theirs rows and/or columns, and check the corresponding columns in datExpr (check their variance).

Also make sure you run the most recent version of WGCNA.

I don't have any other bright ideas, it is hard to debug a problem without the actual data.

I assume you have transposed the expression data before you calculate adjacency (WGCNA requires samples in rows and genes in columns).

I took your data, loaded it into R and am getting no missing or NaN results, so your description or code are missing something. Here's the data I have, transposed into genes in columns format.

> dput(data0)
structure(c(0, 0, 0, 0, 0, 0, 0, 0, 0.018154, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0.021175, 0, 0, 0, 0, 0, 0.016796, 0, 0, 0, 0.001065,
0, 0.008215, 0, 0, 0.006638, 0, 0, 0.006115, 0, 0.004287, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0.008637, 8.347438, 6.54812, 11.78981,
4.094741, 2.713539, 9.611229, 9.865166, 2.525487, 4.12737, 6.10589,
7.512624, 6.973087, 10.63914, 6.62466, 3.451262, 7.860533, 5.780477,
6.401347, 33.63262, 9.634759, 5.701372, 3.655462, 1.862599, 8.341952,
8.780938, 0.005077, 0, 0, 0.005777, 0, 0, 0, 0, 0.011768, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0.089062, 0, 0, 0, 0, 0, 0), .Dim = c(25L,
4L), .Dimnames = list(c("V2", "V3", "V4", "V5", "V6", "V7", "V8",
"V9", "V10", "V11", "V12", "V13", "V14", "V15", "V16", "V17",
"V18", "V19", "V20", "V21", "V22", "V23", "V24", "V25", "V26"
), c("ENSG00000184650.10_2 ", "ENSG00000184647.10_1 ", "ENSG00000000003.14_1 ",
"ENSG00000000005.5_1 ")))

> adjacency(data0, power = 6)
                      ENSG00000184650.10_2  ENSG00000184647.10_1
ENSG00000184650.10_2            1.000000000          7.189324e-03
ENSG00000184647.10_1            0.007189324          1.000000e+00
ENSG00000000003.14_1            0.029619245          5.234850e-10
ENSG00000000005.5_1             0.110284643          4.246166e-08
                      ENSG00000000003.14_1  ENSG00000000005.5_1
ENSG00000184650.10_2           2.961925e-02         1.102846e-01
ENSG00000184647.10_1           5.234850e-10         4.246166e-08
ENSG00000000003.14_1           1.000000e+00         4.387697e-01
ENSG00000000005.5_1            4.387697e-01         1.000000e+00

I don't see why log2(x+1) should cause any trouble. It certainly does not cause any trouble with the data I use. Inspect the transformed data - are all entries of the 4 genes you sent finite after the transformation?